






'''simple docstring'''





from __future__ import annotations

_UpperCamelCase						:						Dict  =					list[tuple[int, int]]

_UpperCamelCase						:						Dict  =					[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_UpperCamelCase						:						List[str]  =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class  snake_case__      :




					def __init__(  self  :		Dict				, _A  :		int				, _A  :		int				, _A  :		int				, _A  :		int				, _A  :		float				, _A  :		Node | None				, )    ->							str:
								UpperCAmelCase_      :       int          =       pos_x
								UpperCAmelCase_      :       Optional[Any]          =       pos_y
								UpperCAmelCase_      :       Tuple          =       (pos_y, pos_x)
								UpperCAmelCase_      :       Any          =       goal_x
								UpperCAmelCase_      :       Dict          =       goal_y
								UpperCAmelCase_      :       Tuple          =       g_cost
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       Tuple          =       self.calculate_heuristic()




					def 		A      (  self  :		Optional[Any]      )    ->							float:
								UpperCAmelCase_      :       Optional[int]          =       abs(self.pos_x - self.goal_x      )
								UpperCAmelCase_      :       List[str]          =       abs(self.pos_y - self.goal_y      )
								return dx + dy





					def __lt__(  self  :		Dict				, _A  :		Tuple      )    ->							bool:
								return self.f_cost < other.f_cost






class  snake_case__      :




					def __init__(  self  :		List[Any]				, _A  :		tuple[int, int]				, _A  :		tuple[int, int]      )    ->							Optional[int]:
								UpperCAmelCase_      :       Dict          =       Node(start[1]				, start[0]				, goal[1]				, goal[0]				, 0				, _A      )
								UpperCAmelCase_      :       Any          =       Node(goal[1]				, goal[0]				, goal[1]				, goal[0]				, 9_99_99				, _A      )

								UpperCAmelCase_      :       List[str]          =       [self.start]
								UpperCAmelCase_      :       list[Node]          =       []

								UpperCAmelCase_      :       Union[str, Any]          =       False




					def 		A      (  self  :		str      )    ->							Path | None:
								while self.open_nodes:
											# Open Nodes are sorted using __lt__
											self.open_nodes.sort()
											UpperCAmelCase_      :       Union[str, Any]          =       self.open_nodes.pop(0      )

											if current_node.pos == self.target.pos:
														UpperCAmelCase_      :       Optional[int]          =       True
														return self.retrace_path(_A      )

											self.closed_nodes.append(_A      )
											UpperCAmelCase_      :       List[str]          =       self.get_successors(_A      )

											for child_node in successors:
														if child_node in self.closed_nodes:
																	continue

														if child_node not in self.open_nodes:
																	self.open_nodes.append(_A      )
														else:
																	# retrieve the best current path
																	UpperCAmelCase_      :       Union[str, Any]          =       self.open_nodes.pop(self.open_nodes.index(_A      )      )

																	if child_node.g_cost < better_node.g_cost:
																				self.open_nodes.append(_A      )
																	else:
																				self.open_nodes.append(_A      )

								if not self.reached:
											return [self.start.pos]
								return None




					def 		A      (  self  :		Any				, _A  :		Node      )    ->							list[Node]:
								UpperCAmelCase_      :       Tuple          =       []
								for action in delta:
											UpperCAmelCase_      :       List[Any]          =       parent.pos_x + action[1]
											UpperCAmelCase_      :       List[str]          =       parent.pos_y + action[0]

											if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(_A      ) - 1):
														continue

											if grid[pos_y][pos_x] != 0:
														continue

											successors.append(
											    Node(
											        _A				, _A				, self.target.pos_y				, self.target.pos_x				, parent.g_cost + 1				, _A				, )      )
								return successors





					def 		A      (  self  :		Dict				, _A  :		Node | None      )    ->							Path:
								UpperCAmelCase_      :       List[str]          =       node
								UpperCAmelCase_      :       List[Any]          =       []
								while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)      )
											UpperCAmelCase_      :       str          =       current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					(0, 0)
						_UpperCamelCase						:						Dict  =					(len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						print('------')

						_UpperCamelCase						:						int  =					GreedyBestFirst(init, goal)
						_UpperCamelCase						:						Union[str, Any]  =					greedy_bf.search()
						if path:
												for pos_x, pos_y in path:
																		_UpperCamelCase						:						Tuple  =					2

												for elem in grid:
																		print(elem)










'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


